Identification of novel therapeutic targets for neuropathic pain based on gene expression patterns

Functional investigation and two-sample Mendelian randomization study of neuropathic pain hub genes obtained by WGCNA analysis

Objective

Neuropathic pain as a complex chronic disease that occurs after neurological injury, however the underlying mechanisms are not clarified in detail, hence therapeutic options are limited. The purpose of this study was to explore potential hub genes for neuropathic pain and evaluate the clinical application of these genes in predicting neuropathic pain.

Methods

Differentially expressed analysis and weighted gene co-expression network analysis (WGCNA) was used to explore new neuropathic pain susceptibility modules and hub genes. KEGG and GO analyses was utilized to explore the potential role of these hub genes. Nomogram model and ROC curves was established to evaluate the diagnostic efficacy of hub genes. Additionally, the correlation of IL-2 with immune infiltration was explored. Finally, a Mendelian randomization study was conducted to determine the causal effect of IL-2 on neuropathic pain based on genome-wide association studies.

Results

WGCNA was performed to establish the networks of gene co-expression, screen for the most relevant module, and screen for 440 overlapping WGCNA-derived key genes. GO and KEGG pathway enrichment analyses demonstrated that the key genes were correlated with cytokine receptor binding, chemokine receptor binding, positive regulation of JAK-STAT cascade, chemokine-mediated signaling pathway, PI3K-AKT pathway and chemokine pathway. Through Cytoscape software, top ten up-regulated genes with high scores were IL2, SMELL, CCL4, CCR3, CXCL1, CCR1, HGF, CXCL2, GATA3, and CRP. In addition, nomogram model performed well in predicting neuropathic pain risk, and with the ROC curve, the model was showed to be effective in diagnosis. Finally, IL2 was selected and we observed that IL2 was causally associated with immune cell infiltrates in trigeminal neuralgia. In inverse variance weighting, we found that IL2 was associated with the risk of trigeminal neuralgia with an OR of 1.203 (95% CI = 1.004-1.443, p = 0.045).

Conclusion

We constructed a WGCNA-based co-expression network and identified neuropathic pain-related hub genes, which may offer further insight into pre-symptomatic diagnostic approaches and may be useful for the study of molecular mechanisms for understanding neuropathic pain risk genes.

scRNA-sequencing reveals subtype-specific transcriptomic perturbations in DRG neurons of PirtEGFPf mice in neuropathic pain condition

Functionally distinct subtypes/clusters of dorsal root ganglion (DRG) neurons may play different roles in nerve regeneration and pain. However, details about their transcriptomic changes under neuropathic pain conditions remain unclear. Chronic constriction injury (CCI) of the sciatic nerve represents a well-established model of neuropathic pain, and we conducted single-cell RNA-sequencing (scRNA-seq) to characterize subtype-specific perturbations of transcriptomes in lumbar DRG neurons on day 7 post-CCI. By using Pirt^EGFPf mice that selectively express an enhanced green fluorescent protein in DRG neurons, we established a highly efficient purification process to enrich neurons for scRNA-seq. We observed the emergence of four prominent CCI-induced clusters and a loss of marker genes in injured neurons. Importantly, a portion of injured neurons from several clusters were spared from injury-induced identity loss, suggesting subtype-specific transcriptomic changes in injured neurons. Moreover, uninjured neurons, which are necessary for mediating the evoked pain, also demonstrated cell-type-specific transcriptomic perturbations in these clusters, but not in others. Notably, male and female mice showed differential transcriptomic changes in multiple neuronal clusters after CCI, suggesting transcriptomic sexual dimorphism in DRG neurons after nerve injury. Using Fgf3 as a proof-of-principle, RNAscope study provided further evidence of increased Fgf3 in injured neurons after CCI, supporting scRNA-seq analysis, and calcium imaging study unraveled a functional role of Fgf3 in neuronal excitability. These findings may contribute to the identification of new target genes and the development of DRG neuron cell-type-specific therapies for optimizing neuropathic pain treatment and nerve regeneration.

Bioinformatics Analysis of the MicroRNA-Metabolic Gene Regulatory Network in Neuropathic Pain and Prediction of Corresponding Potential Therapeutics

Neuropathic pain (NP) involves metabolic processes that are regulated by metabolic genes and their non-coding regulator genes such as microRNAs (miRNAs). Here, we aimed at exploring the key miRNA signatures regulating metabolic genes involved in NP pathogenesis. We downloaded NP-related data from public databases and identified differentially expressed microRNAs (miRNAs) and mRNAs through differential gene expression analysis. The miRNA target prediction was performed, and integration with the differentially expressed metabolic genes (DEMGs) was used for constructing the miRNA-DEMG network. Subsequently, functional enrichment analysis and protein–protein interaction (PPI) analysis were performed to explore the role of DEMGs in the regulatory network. The drug prediction was performed based on the DEMGs in the miRNA-DEMG network. A total of 8251 differentially expressed mRNAs (4193 upregulated and 4058 downregulated), and 959 differentially expressed miRNAs (455 upregulated and 504 downregulated) were identified. Moreover, after target gene prediction, a miRNA-DEMG network composed of 22 miRNAs and 113 mRNAs was constructed. The network was constituted of 135 nodes and 236 edges. We found that DEMGs in the network were mainly enriched in metabolic pathways and metabolic processes. A total of 1200 drugs were predicted as potential therapeutics for NP based on the differentially expressed genes, while 170 drugs were predicted for the DEMGs in the miRNA-DEMG network. Conclusively, our study predicted drugs that may be effective against the metabolic changes induced by miRNA dysregulation in NP. This information will help further reveal the pathological mechanism of NP and provide more treatment options for NP patients.